

from __future__ import annotations

from random import choice
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str       )					->				Optional[Any]:
	return choice(a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[int]			,a__    :   int       )					->				int:
	__A :			List[str]		    =			random_pivot(a__       )

	# partition based on pivot
	# linear time
	__A :			Optional[int]		    =			[e for e in lst if e < pivot]
	__A :			Tuple		    =			[e for e in lst if e > pivot]

	# if we get lucky, pivot might be the element we want.
	# we can easily see this:
	# small (elements smaller than k)
	# + pivot (kth element)
	# + big (elements larger than k)
	if len(a__       ) == k - 1:
		return pivot
	# pivot is in elements bigger than k
	elif len(a__       ) < k - 1:
		return kth_number(a__			,k - len(a__       ) - 1       )
	# pivot is in elements smaller than k
	else:
		return kth_number(a__			,a__       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()











'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TrajectoryTransformerModel",
      "TrajectoryTransformerPreTrainedModel",
      "load_tf_weights_in_trajectory_transformer",
  ]


if TYPE_CHECKING:
  from .configuration_trajectory_transformer import (
      TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TrajectoryTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trajectory_transformer import (
        TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TrajectoryTransformerModel,
        TrajectoryTransformerPreTrainedModel,
        load_tf_weights_in_trajectory_transformer,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



